A Profit Maximization Scheme With Guaranteed Quality Of Service
In Cloud Computing
1.Gosikonda Neeraja 2. Katukoori Shekhar 3.V Janaki
1.PG Scholar, Department of CSE, Vaagdevi College Of Engineering Bollikunta,Warangal, Telangana Mail ID : neeraja.nvs07@gmail.com
2. Assistant Professor Department of CSE, Vaagdevi College of Engineering, Bollikunta, Warangal ,Telangana Mail ID : .shekhar.ktk@gmail.com
3. Professor ,HOD Department of CSE, Vaagdevi College of Engineering,Bollikunta, Warangal ,Telangana
Abstract:
An effective and efficient way to provide
computing resources and services to customers
on demand, cloud computing has become more
and more popular. From cloud service
providers’ perspective, profit is one of the most
important considerations, and it is mainly
determined by the configuration of a cloud
service platform under given market demand.
However, a single long-term renting scheme is
usually adopted to configure a cloud platform,
which cannot guarantee the service quality but
leads to serious resource waste. In this paper, a
double resource renting scheme is designed
firstly in which short-term renting and long-term
renting are combined aiming at the existing
issues. This double renting scheme can
effectively guarantee the quality of service of all
requests and reduce the resource waste greatly.
Secondly, a service system is considered as an
M/M/m+D queuing model and the performance
indicators that affect the profit of our double
renting scheme are analyzed, e.g., the average
charge, the ratio of requests that need temporary
servers, and so forth. Thirdly, a profit
maximization problem is formulated for the
double renting scheme and the optimized
configuration of a cloud platform is obtained by
solving the profit maximization problem. Finally,
a series of calculations are conducted to
compare the profit of our proposed scheme with
that of the single renting scheme. The results
show that our scheme can not only guarantee the
service quality of all requests, but also obtain
more profit than the latter.
1. INTRODUCTION
Cloud computing is quickly becoming an
effective and efficient way of computing
resources. By centralized management of
hosted services over the Internet. Cloud
computing is able to provide the most
cost-effective and energy-efficient way of computing
resources management. Cloud computing turn’s
information technology into ordinary
commodities and utilities by using the
pay-peruse pricing model. A service provider rents
resources from the infrastructure vendors, builds
appropriate multi server systems, and provides
various services to users. A consumer submits a
service request to a service provider, receives the
desired result from the service provider with
certain service-level agreement. Then pays for
the service based on the amount of the service
and the quality of the service. A service provider
can build different multi server systems for
different application domains, such that service
requests of different nature are sent to different
multi server systems. Owing to redundancy of
computer system networks and storage system
cloud may not be reliable for data, the security
score is concerned. In cloud computing security
is tremendously improved because of a superior
technology security system, which is now easily
available and affordable. Applications no longer
run on the desktop Personal Computer but run in
the cloud. This means that the PC does not need
the processing power or hard disk space as
demanded by traditional desktop software.
Powerful servers and the like are no longer
required. The computing power of the cloud can
be used to replace or supplement internal
computing resources. Organizations no longer
have to purchase computing resources to handle
the capacity peaks. Cloud computing is quickly
becoming an effective and efficient way of
computing resources. By centralized
management of resources and services, cloud
computing delivers hosted services over the
Internet. Cloud computing is able to provide the
most cost-effective and energy-efficient way of
computing resources management. Cloud
computing turn’s information technology into
ordinary commodities and utilities by using the
pay-per-use pricing model. A service provider
rents resources from the infrastructure vendors,
builds appropriate multi server systems, and
provides various services to users. A consumer
submits a service request to a service provider,
receives the desired result from the service
provider with certain service-level agreement.
Then pays for the service based on the amount of
the service and the quality of the service. A
service provider can build different multi server
systems for different application domains, such
that service requests of different nature are sent
to different multi server systems. Owing to
storage system cloud may not be reliable for
data, the security score is concerned. In cloud
computing security is tremendously improved
because of a superior security system, which is
now easily available and affordable. Applications
no longer run on the desktop Personal Computer
but run in the cloud. This means that the PC does
not need the processing power or hard disk space
as demanded by traditional desktop software.
Powerful servers and the like are no longer
required. The computing power of the cloud can
be used to replace or supplement internal
computing resources. Organizations no longer
have to purchase computing resources to handle
the capacity peaks.
2. LITERATURE SURVEY
Existing clouds focus on the provision of web
services targeted to developers, such as Amazon
Elastic Compute Cloud (EC2) [4], or the
deployment of servers, such as Go Grid [1].
Emerging clouds such as the Amazon Simple DB
and Simple Storage Service offer data
management services. Optimal pricing of cached
structures is central to maximizing profit for a
cloud that offers data services. Cloud businesses
may offer their services for free, such as Google
Apps [2] and Microsoft Azure [3] or based on a
pricing scheme. Amazon Web Service (AWS)
clouds include separate prices for infrastructure
elements, i.e. disk space, CPU, I/O and
bandwidth. Pricing schemes are static, and give
the option for pay as-you-go. Static pricing
cannot guarantee cloud profit maximization. The
cloud caching service can maximize its profit
using an optimal pricing scheme. This work
proposes a pricing scheme along the insight that
it is sufficient to use a simplified price-demand
model which can be re-evaluated in order to
adapt to model mismatches, external
disturbances and errors, employing feedback
from the real system behavior and performing
refinement of the optimization procedure.
Overall, optimal pricing necessitates an
appropriately simplified price-demand model
that incorporates the correlations of structures in
the cache services
3 PROFIT MAXIMIZATION IN CLOUD
COMPUTING
From this Paper We ReferredWe have proposed
a pricing model for cloud computing which takes
many factors into considerations, such as the
requirement r of a service, the workload of an
application environment, the configuration (m
and s) of a multi-server system, the service level
agreement c, the satisfaction (r and s) of a
penalty d of a low-quality service, the cost of
renting, the cost of energy consumption, and a
service provider’s margin and profit a. By using
an M/M/ m queuing model, we formulated and
solved the problem of optimal multiserver
configuration for profit maximization in a cloud
computing environment. Our discussion can be
easily extended to other service charge functions.
Our methodology can be applied to other pricing
models. At three-tier cloud structure, which
consists of infrastructure vendors, service
providers and consumers, the latter two parties
are particular interest to us. Clearly, scheduling
strategies in this scenario should satisfy the
objectives of both parties. Our contributions
include the development of a pricing model using
processor-sharing for clouds, the application of
this pricing model to composite services with
dependency consideration, and the development
of two sets of profit-driven scheduling
algorithms. From this Paper We ReferredA
pricing model is developed for cloud computing
which takes many factors into considerations,
such as the requirement r of a service, the
workload of an application environment, the
configuration (m and s) of a multi-server system,
the service level agreement c, the satisfaction (r
ands0) of a consumer, the quality (W and T) of a
service, the penalty d of a low-quality service,
the cost of renting, the cost of energy
consumption, and a service provider’s margin
and profit. And this will schedules the job
according to optimization of speed and size of the input hereby maximizing the profit. •
4. PROPOSED MECHANISM
In this section, we first propose the
Double-QualityGuaranteed (DQG) resource renting
scheme which combines long-term renting with
short-term renting. The IJARCCE ISSN (Online)
2278-1021 ISSN (Print) 2319 5940 International
Journal of Advanced Research in Computer and
Communication Engineering Vol. 4, Issue 12,
December 2015 Copyright to IJARCCE DOI
10.17148/IJARCCE.2015.41294 403 main
computing capacity is provided by the long-term
rented servers due to their low price. The
short-term rented servers provide the extra capacity in
peak period Advantages: In proposed system we
are using the Double-QualityGuaranteed (DQG)
renting scheme can achieve more profit than the
compared Single-Quality-Unguaranteed (SQU)
renting scheme in the premise of guaranteeing
the service quality completely. Cloud
Computing: Cloud computing describes a type of
outsourcing of computer services, similar to the
way in which the supply of electricity is
need to worry where the electricity is from, how
it is made, or transported. Every month, they pay
for what they consumed. The idea behind cloud
computing is similar: The user can simply use
storage, computing power, or specially crafted
development environments, without having to
worry how these work internally. Cloud
computing is usually Internet-based computing.
The cloud is a metaphor for the Internet based on
how the internet is described in computer
network diagrams; which means it is an
abstraction hiding the complex infrastructure of
the internet. It is a style of computing in which
IT-related capabilities are provided “as a
service”, allowing users to access
technology-enabled services from the Internet ("in the
cloud")without knowledge of, or control over the
technologies behind these servers. Queuing
model: We consider the cloud service platform as
a multiserver system with a service request
queue. The clouds provide resources for jobs in
the form of virtual machine (VM). In addition,
the users submit their jobs to the cloud in which
a job queuing system such as SGE, PBS, or
Condor is used. All jobs are scheduled by the job
scheduler and assigned to different VMs in a
centralized way. Hence, we can consider it as a
service request queue. For example, Condor is a
specialized workload management system for
compute intensive jobs and it provides a job
queuing mechanism, scheduling policy, priority
scheme, resource monitoring, and resource
management. Users submit their jobs to Condor,
and Condor places them into a queue, chooses
when and where to run them based upon a
policy. An M/M/m+D queuing model is build for
our multiserver system with varying system size.
And then, an optimal configuration problem of
profit maximization is formulated in which many
factors are taken into considerations, such as the
market demand, the workload of requests, the
server-level agreement, the rental cost of servers,
the cost of energy consumption, and so forth.
The optimal solutions are solved for two
different situations, which are the ideal optimal
solutions and the actual optimal solutions.
Business Service Providers Module: Service
providers pay infrastructure providers for renting
their physical resources, and charge customers
for processing their service requests, which
generates cost and revenue, respectively. The
profit is generated from the gap between the
revenue and the cost.In this module the service
providers considered as cloud brokers because
they can play an important role in between cloud
customers and infrastructure providers ,and he
can establish an indirect connection between
Infrastructure Service Provider Module: In the
three-tier structure, an infrastructure provider the
basic hardware and software facilities. A service
provider rents resources from infrastructure
providers and prepares, a set of services in the
form of virtual machine (VM). Infrastructure
providers provide two kinds of resource renting
schemes, e.g., long-term renting and short-term
renting. In general, the rental price of long-term
renting is much cheaper than that of short-term
renting. Cloud Customers: A customer submits a
service request to a service provider which
delivers services on demand. The customer
receives the desired result from the service
provider with certain service-level agreement,
and pays for the service based on the amount of
the service and the service quality.
5. CONCLUSION
Maximize the profit of service providers, this
paper has proposed a novel
Double-Quality-Guaranteed (DQG) renting scheme for service
providers. This scheme combines short-term
renting with long-term renting, which can reduce
the resource waste greatly and adapt to the
dynamical demand of computing capacity. An
M/M/m+D queuing model is build for our
multiserver system with varying system size.
And then, an optimal configuration problem of
profit maximization is formulated in which many
factors are taken into considerations, such as the
market demand, the workload of requests, the
server- agreement, the rental cost of servers, the
cost of energy consumption, and so forth. The
optimal solutions are solved for two different
situations, which are the ideal optimal solutions
and the actual optimal solutions. In addition, a
series of calculations are conducted to compare
the profit obtained by the DQG renting scheme
with the Single-Quality-Unguaranteed (SQU)
renting scheme. The results show that our
scheme outperforms the SQU scheme in terms of
both of service quality and profit.
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G.NEERAJA PG Scholar, Department of CSE, Vaagdevi College Of Engineering
Bollikunta,Warangal, Telangana Mail ID : neeraja.nvs07@gmail.com
K SHEKHAR.Assistant Professor Department of CSE, Vaagdevi College of Engineering,